苹果对苹果:从质量比转向每电极面积添加分子,优化锂离子电池

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-05-30 DOI:10.1039/D4DD00002A
Bojing Zhang, Leon Merker, Monika Vogler, Fuzhan Rahmanian and Helge S. Stein
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引用次数: 0

摘要

液态电解质电池中的电解质添加剂可促使在电极上形成保护性中间相(SEI),从而抑制电极上的副反应。过去几年中,对不同添加剂用量的研究使人们对电解质配方对电池寿命的影响有了全面的了解。然而,这些研究大多侧重于电解液中添加剂质量分数的变化,而忽略了添加剂的物质的量 (nadd) 与电极面积 (Aelectrode) 的比率 (radd)。在此,我们利用极其精确的自动电池装配系统 (AUTOBASS) 来改变电极面积和添加剂的物质的量。这些数据有力地证明,仅报告电解质成分的质量比是不够的,还应报告添加剂相对于电极面积的摩尔数。本文研究了两种最常用的添加剂,即碳酸氟乙烯酯(FEC)和碳酸乙烯酯(VC)。在 1 mAh/cm2 和 3 mAh/cm2 两种质量负载下,每种添加剂的变化范围分别为 VC 0.1 wt.-% - 3.0 wt.-%,FEC 5 wt.-% - 15 wt.-%。为了让社会各界参与寻找更好的描述符(如建议的 radd),我们在发表本手稿的同时还公布了数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Apples to apples: shift from mass ratio to additive molecules per electrode area to optimize Li-ion batteries

Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective solid electrolyte interphase (SEI) at the electrodes e.g. to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last few years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focused on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive's amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. Our data provides evidence that reporting the mass ratios of electrolyte components is insufficient and the amount of substance of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt-%–3.0 wt-% for VC, and 5 wt-%–15 wt-% for FEC for two electrode loadings of 1 mA h cm−2 and 3 mA h cm−2. To help the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript. The active electrode placement correction reduces the failure rate of our automatically assembled cells to 3%.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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